fitVsDatCorrelation=0.894205304991652 cont.fitVsDatCorrelation=0.23340682678238 fstatistic=11298.0036252531,55,761 cont.fstatistic=2383.63206827364,55,761 residuals=-0.802748173890515,-0.0804749685877999,-0.00561476156084932,0.0739696975326815,0.948532546529754 cont.residuals=-0.561876370481649,-0.207001739683163,-0.0627140619167168,0.113632769680714,1.48269864440106 predictedValues: Include Exclude Both Lung 57.597470112898 59.1234475793246 78.5085312285316 cerebhem 61.436094388095 67.4442183678775 72.9983124200919 cortex 60.0323798502566 56.3718353224029 89.4458756761777 heart 58.1019147957104 55.1505516171142 81.0960310946488 kidney 53.6006766725793 58.3041740456667 79.5592169365509 liver 53.041432930649 55.1793763870516 70.8361512512521 stomach 57.4449881197038 58.6862686929431 72.6608965290784 testicle 53.7285231331966 58.5471407826051 71.5831945833777 diffExp=-1.52597746642663,-6.00812397978248,3.66054452785367,2.95136317859623,-4.70349737308742,-2.13794345640262,-1.24128057323929,-4.81861764940852 diffExpScore=1.82462228029621 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 58.6514619968771 68.3726884388064 59.1333957108272 cerebhem 63.2135771385665 58.884302971016 61.9748252945875 cortex 59.2997456083358 65.8666113448615 63.4312340124484 heart 59.6845447355842 62.3104463102749 63.6346341723924 kidney 60.2225332444491 61.7289568031031 57.091084161823 liver 60.4919796439016 63.2660158896602 62.9348809745143 stomach 54.5994607766288 62.6469101982682 54.3467770645799 testicle 60.8878732628845 69.4520399817207 63.4660920640126 cont.diffExp=-9.72122644192925,4.32927416755052,-6.56686573652571,-2.62590157469069,-1.50642355865401,-2.77403624575858,-8.04744942163939,-8.56416671883623 cont.diffExpScore=1.20995671970968 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.510836293282728 cont.tran.correlation=-0.190696129680416 tran.covariance=0.00172998206493263 cont.tran.covariance=-0.000439180073516108 tran.mean=57.7369057998796 cont.tran.mean=61.8486967715587 weightedLogRatios: wLogRatio Lung -0.106336072973898 cerebhem -0.388575690875367 cortex 0.25564814788514 heart 0.210411154227398 kidney -0.338434508129854 liver -0.157701314060838 stomach -0.0868271644426856 testicle -0.345862324778147 cont.weightedLogRatios: wLogRatio Lung -0.636186155202825 cerebhem 0.291656384269145 cortex -0.434297587583815 heart -0.176985977273666 kidney -0.101553792523843 liver -0.184951622824787 stomach -0.559416518928804 testicle -0.549418389543249 varWeightedLogRatios=0.060550177511875 cont.varWeightedLogRatios=0.0968674205544693 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93983753204682 0.0723169484795858 54.4801407537126 7.63278282590147e-265 *** df.mm.trans1 0.0805457327398816 0.0633240514111424 1.27196114185627 0.203775477672271 df.mm.trans2 0.148471242866297 0.0567870873864699 2.61452470446075 0.0091119366235576 ** df.mm.exp2 0.268963119231609 0.0748885931273169 3.59150984148346 0.000349885481472587 *** df.mm.exp3 -0.136678898430316 0.0748885931273169 -1.8250963561026 0.0683783476723574 . df.mm.exp4 -0.0932675915386858 0.0748885931273169 -1.24541786197162 0.213361518032371 df.mm.exp5 -0.0991651702645443 0.0748885931273169 -1.32416922422291 0.185844418787022 df.mm.exp6 -0.0486057904438875 0.0748885931273169 -0.649041308083509 0.516507499082353 df.mm.exp7 0.0673312340857494 0.0748885931273169 0.899085311581173 0.36889161231021 df.mm.exp8 0.0130170007142338 0.0748885931273169 0.173818203422566 0.862054563652557 df.mm.trans1:exp2 -0.204444245401001 0.0702517274870743 -2.91016680605637 0.00371776659942483 ** df.mm.trans2:exp2 -0.137289847436215 0.0560414915319867 -2.44978932007643 0.0145182876815583 * df.mm.trans1:exp3 0.178084334170836 0.0702517274870743 2.53494597984941 0.0114456913413178 * df.mm.trans2:exp3 0.0890209686085784 0.0560414915319867 1.58848321440139 0.112592470110860 df.mm.trans1:exp4 0.101987566664209 0.0702517274870743 1.45174460916956 0.146984754132305 df.mm.trans2:exp4 0.0237067494308233 0.0560414915319867 0.423021386168715 0.672399169687666 df.mm.trans1:exp5 0.0272482176638508 0.0702517274870743 0.387865446708968 0.698224075401509 df.mm.trans2:exp5 0.0852112671577725 0.0560414915319867 1.52050319911876 0.128799918049725 df.mm.trans1:exp6 -0.0337994930917378 0.0702517274870743 -0.481119743254095 0.630569697186505 df.mm.trans2:exp6 -0.0204325321424880 0.0560414915319867 -0.364596508478469 0.715513911450914 df.mm.trans1:exp7 -0.0699821176782942 0.0702517274870743 -0.996162232326177 0.319487964306067 df.mm.trans2:exp7 -0.0747530479562007 0.0560414915319867 -1.3338875521101 0.182639769468319 df.mm.trans1:exp8 -0.0825516282568002 0.0702517274870744 -1.17508325004519 0.240328943859782 df.mm.trans2:exp8 -0.0228123355165818 0.0560414915319867 -0.407061534105695 0.6840772608993 df.mm.trans1:probe2 -0.152414634515979 0.0430202214730969 -3.54286029446158 0.000419942705847739 *** df.mm.trans1:probe3 -0.194622200383992 0.0430202214730969 -4.52397021028125 7.03867144619332e-06 *** df.mm.trans1:probe4 -0.0704719858725683 0.0430202214730969 -1.63811304218038 0.101811482357920 df.mm.trans1:probe5 0.136228496621995 0.0430202214730969 3.16661541845356 0.00160375874028208 ** df.mm.trans1:probe6 -0.214419575105082 0.0430202214730969 -4.98415786258031 7.71007001072867e-07 *** df.mm.trans1:probe7 -0.135356282831942 0.0430202214730969 -3.14634091125237 0.00171765616592724 ** df.mm.trans1:probe8 -0.207794871545520 0.0430202214730969 -4.83016740570399 1.64979842397947e-06 *** df.mm.trans1:probe9 0.739092059674621 0.0430202214730969 17.1801081995084 3.75664754463085e-56 *** df.mm.trans1:probe10 0.51688576139574 0.0430202214730969 12.0149488704743 1.42358149517043e-30 *** df.mm.trans1:probe11 0.105166764985175 0.0430202214730969 2.44458911144710 0.0147275887082039 * df.mm.trans1:probe12 0.105793238575702 0.0430202214730969 2.4591514165463 0.0141480932953065 * df.mm.trans1:probe13 0.268428790468490 0.0430202214730969 6.23959573607388 7.27843101496779e-10 *** df.mm.trans1:probe14 0.144829383249183 0.0430202214730969 3.36654201884463 0.000799386027518215 *** df.mm.trans1:probe15 0.184136404756446 0.0430202214730969 4.28022912135859 2.10544935311214e-05 *** df.mm.trans1:probe16 0.221624094726108 0.0430202214730969 5.15162607576772 3.29280203564485e-07 *** df.mm.trans1:probe17 -0.0638951608874441 0.0430202214730969 -1.48523551714864 0.137895556484907 df.mm.trans1:probe18 -0.117337154383703 0.0430202214730969 -2.72748838490013 0.0065288865049233 ** df.mm.trans1:probe19 -0.0966248389049267 0.0430202214730969 -2.24603304205098 0.0249880120693565 * df.mm.trans1:probe20 -0.0622490790279169 0.0430202214730969 -1.44697253748089 0.148316392790498 df.mm.trans1:probe21 -0.0915244367355991 0.0430202214730969 -2.12747479212386 0.0337015827485146 * df.mm.trans1:probe22 -0.0888041258732539 0.0430202214730969 -2.06424148533936 0.0393332103400755 * df.mm.trans2:probe2 0.0608083268842745 0.0430202214730969 1.41348242296478 0.157922835413671 df.mm.trans2:probe3 -0.0723629776856598 0.0430202214730969 -1.68206892498014 0.0929657221711973 . df.mm.trans2:probe4 -0.126260485548071 0.0430202214730969 -2.93491017072121 0.00343687362865967 ** df.mm.trans2:probe5 0.0910616324440977 0.0430202214730969 2.11671696067497 0.0346078431163555 * df.mm.trans2:probe6 -0.0574207164810993 0.0430202214730969 -1.33473781665694 0.182361356971016 df.mm.trans3:probe2 0.938449611186465 0.0430202214730969 21.8141510910941 2.55206264638241e-82 *** df.mm.trans3:probe3 -0.187755063220227 0.0430202214730969 -4.36434441272325 1.45113856555295e-05 *** df.mm.trans3:probe4 0.467752719638704 0.0430202214730969 10.8728570802737 1.07434747602121e-25 *** df.mm.trans3:probe5 -0.272034831342017 0.0430202214730969 -6.32341773303367 4.35906758653254e-10 *** df.mm.trans3:probe6 0.219704299541865 0.0430202214730969 5.10700066198542 4.14057956473276e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16245928265663 0.157083391768767 26.4984046740213 3.91404875821741e-110 *** df.mm.trans1 -0.118426734936308 0.137549453970807 -0.860975681964119 0.389522618429807 df.mm.trans2 0.0943230503548462 0.123350175621060 0.76467706575962 0.444700916366818 df.mm.exp2 -0.121424719891587 0.162669394388936 -0.746450924881821 0.455625598896635 df.mm.exp3 -0.0965098366089564 0.162669394388936 -0.593288227152337 0.553164556749843 df.mm.exp4 -0.148745809846539 0.162669394388936 -0.914405628700463 0.360793373482448 df.mm.exp5 -0.0406383538785593 0.162669394388936 -0.249821756767561 0.802792614003449 df.mm.exp6 -0.109031576179871 0.162669394388936 -0.670264843546297 0.502892316315239 df.mm.exp7 -0.0746370629536852 0.162669394388936 -0.458826709437614 0.646489626658907 df.mm.exp8 -0.0176254276586560 0.162669394388936 -0.108351221966895 0.913745653710399 df.mm.trans1:exp2 0.196331323842227 0.15259741821656 1.28659662880798 0.198626169917646 df.mm.trans2:exp2 -0.0279741810678739 0.121730628223481 -0.229803965330048 0.81830585672578 df.mm.trans1:exp3 0.107502350421629 0.15259741821656 0.704483415761767 0.481347303422287 df.mm.trans2:exp3 0.0591680402883341 0.121730628223481 0.486057134115084 0.627066584803311 df.mm.trans1:exp4 0.166206412306522 0.15259741821656 1.08918233512082 0.276418276519709 df.mm.trans2:exp4 0.055901446056163 0.121730628223481 0.459222521660985 0.646205521511736 df.mm.trans1:exp5 0.067072440272587 0.15259741821656 0.43953849977593 0.66039617850495 df.mm.trans2:exp5 -0.0615819622687573 0.121730628223481 -0.505887163875478 0.613082368087915 df.mm.trans1:exp6 0.139929862287589 0.15259741821656 0.916987088792068 0.359439911785584 df.mm.trans2:exp6 0.0314064343206506 0.121730628223481 0.257999443352848 0.79647709125166 df.mm.trans1:exp7 0.00304856749807183 0.15259741821656 0.0199778445382702 0.98406628325316 df.mm.trans2:exp7 -0.0128220284438093 0.121730628223481 -0.105331161359570 0.916140793697107 df.mm.trans1:exp8 0.0550469548704297 0.15259741821656 0.360733199249212 0.71839901299532 df.mm.trans2:exp8 0.033288416754547 0.121730628223481 0.273459664509690 0.784574092505324 df.mm.trans1:probe2 0.0121607398041706 0.0934464526741646 0.130135916946719 0.896493284432723 df.mm.trans1:probe3 -0.0309043968658721 0.0934464526741646 -0.330717710319423 0.740948757313551 df.mm.trans1:probe4 0.0855805266505792 0.0934464526741646 0.915824241600557 0.360049197465195 df.mm.trans1:probe5 0.0677602517223525 0.0934464526741646 0.725123852037739 0.468598957044607 df.mm.trans1:probe6 0.00806509806984517 0.0934464526741646 0.0863071613640285 0.931244948394082 df.mm.trans1:probe7 -0.0387928858312069 0.0934464526741646 -0.415134921883793 0.678160148579814 df.mm.trans1:probe8 0.069490746358386 0.0934464526741646 0.743642421619695 0.45732235966606 df.mm.trans1:probe9 -0.0202669090316626 0.0934464526741647 -0.216882593738798 0.82835797215518 df.mm.trans1:probe10 0.125527353054532 0.0934464526741647 1.34330784596210 0.179572754852251 df.mm.trans1:probe11 0.172872829140651 0.0934464526741647 1.84996673702998 0.0647058265392663 . df.mm.trans1:probe12 -0.0767855192667273 0.0934464526741647 -0.821706090165543 0.411501550412107 df.mm.trans1:probe13 0.0460015164875251 0.0934464526741646 0.492276754987439 0.622665692476147 df.mm.trans1:probe14 0.000687994701298095 0.0934464526741646 0.00736244856396038 0.994127598590205 df.mm.trans1:probe15 0.0387785223337113 0.0934464526741646 0.414981213561171 0.678272619747555 df.mm.trans1:probe16 -0.0113761062256349 0.0934464526741646 -0.121739305239353 0.903137622112053 df.mm.trans1:probe17 0.0608934865757254 0.0934464526741647 0.651640429712756 0.514829901374309 df.mm.trans1:probe18 0.111633255760693 0.0934464526741646 1.19462272313261 0.232606660391392 df.mm.trans1:probe19 -0.0148920651146742 0.0934464526741646 -0.159364691633623 0.873423840897878 df.mm.trans1:probe20 0.155523424765691 0.0934464526741646 1.66430528195629 0.0964630964173026 . df.mm.trans1:probe21 0.0599595296385012 0.0934464526741646 0.641645861588477 0.521296359656601 df.mm.trans1:probe22 -0.0496786652868187 0.0934464526741646 -0.531627085514327 0.595139447385605 df.mm.trans2:probe2 -0.0408917981079744 0.0934464526741646 -0.437596044983737 0.661803302220032 df.mm.trans2:probe3 -0.0288361332720495 0.0934464526741646 -0.308584568454378 0.757722014520573 df.mm.trans2:probe4 -0.0981592157486907 0.0934464526741646 -1.05043276592809 0.293852643277481 df.mm.trans2:probe5 -0.0907946850305144 0.0934464526741646 -0.971622597030016 0.331547055053397 df.mm.trans2:probe6 -0.123024727258705 0.0934464526741646 -1.31652645700395 0.188393760719553 df.mm.trans3:probe2 -0.00100287153290994 0.0934464526741646 -0.0107320449756057 0.99144004413285 df.mm.trans3:probe3 -0.100367421771070 0.0934464526741646 -1.07406347591425 0.283134672775835 df.mm.trans3:probe4 -0.0450851672400641 0.0934464526741646 -0.482470612311739 0.629610414892217 df.mm.trans3:probe5 -0.0945593811561204 0.0934464526741646 -1.01190979914279 0.311902805129473 df.mm.trans3:probe6 -0.110343730339212 0.0934464526741646 -1.18082310437150 0.238041926398131